Neural networks are able to capture non-linear dependencies within the data, which enable non-linear forecasting models and this category of models started to receive greater attention in the last years.
In particular, Recurrent Neural Networks (RNNs) are used with successes in multiple domains such as traffic time series prediction [1]} or wind forecasting [2]}, due to their recurrent nature that is able to capture temporal relationships within the data. However they suffer from the well known vanishing gradient problem [3]}. Different models such as Long-Short-Term-Memory (LSTM) or Gated-Recurrent-Unit (GRU) come as an extension to traditional RNNs. The core idea of RNN models is by integrating different gates to control what the network is going to learn and forget leading to better results and an elegant solution for the vanishing problem.
